@article{Genesereth2005,
author = {Genesereth, Michael and Love, Nathaniel and Pell, B},
journal = {AI magazine},
title = {{General Game Playing: Overview of the {AAAI} Competition}},
year = {Spring, 2005}
}

@article{Bjornsson2009,
author = {Bj\"{o}rnsson, Y and Finnsson, H},
journal = {IEEE Transactions on Computational Intelligence and AI in Games},
number = {1},
title = {{CadiaPlayer: A Simulation-Based General Game Player}},
volume = {1},
year = {2009}
}

@article{Karakovskiy2012,
author = {Karakovskiy, Sergey and Togelius, Julian},
journal = {IEEE Transactions on Computational Intelligence and AI in Games},
number = {1},
title = {{The Mario AI Benchmark and Competitions}},
volume = {4},
year = {2012}
}

@inproceedings{Young2012,
author = {Young, Jay and Smith, Fran and Atkinson, Christopher and Poyner, Ken and Chothia, Tom},
title = {{SCAIL: An Integrated Starcraft AI System}},
booktitle = {IEEE Conference on Computational Intelligence and Games},
year = {2012}
}

@inproceedings{Wallraven2003,
author = {Wallraven, Christian and Caputo, Barbara and Graf, Arnulf},
booktitle = {International Conference on Computer Vision},
title = {{Recognition with Local Features: The Kernel Recipe}},
year = {2003}
}

@article{Quinlan1986,
author = {Quinlan, JR},
title = {{Induction of Decision Trees}},
journal = {Journal of Machine Learning},
volume = {1},
number = {1},
year = {1986}
}

@Book{sutton98a,
  author =	 {Richard S. Sutton and Andrew G. Barto},
  title =	 {Reinforcement Learning: An Introduction},
  publisher =	 {{MIT} Press},
  year =	 1998,
  abstract =	 {This introductory textbook on reinforcement learning
                  is targeted toward engineers and scientists in
                  artificial intelligence, operations research, neural
                  networks, and control systems, and we hope it will
                  also be of interest to psychologists and
                  neuroscientists.},
  keywords =     {ai reinforcement learning},
  url = 	 {http://www.cs.ualberta.ca/\%7Esutton/book/ebook/the-book.html},
  isbn = 	 {0262193981},
  googleid = 	 {1Ubs6AcJ6QUJ:scholar.google.com/},
  cluster = 	 {425881569340442325}
}

@phdthesis{Naddaf2010,
abstract = {This research focuses on developing AI agents that play arbitrary Atari 2600 console games without having any game-specific assumptions or prior knowledge. Twomain approaches are considered: reinforcement learning basedmethods and search basedmethods. The RL-based methods use feature vectors generated from the game screen as well as the console RAM to learn to play a given game. The search-based methods use the emulator to simulate the consequence of actions into the future, aiming to play as well as possible by only exploring a very small fraction of the state-space. To insure the generic nature of our methods, all agents are designed and tuned using four specific games. Once the development and parameter selection is complete, the performance of the agents is evaluated on a set of 50 randomly selected games. Significant learning is reported for the RL-based methods on most games. Additionally, some instances of human- level performance is achieved by the search-based methods.},
author = {Naddaf, Yavar},
booktitle = {Computing},
file = {:Users/anna/Documents/Mendeley Desktop/Naddaf\_2010\_Game-Independent AI Agents for Playing Atari 2600 Console Games.pdf:pdf},
school = {University of Alberta},
title = {{Game-Independent AI Agents for Playing Atari 2600 Console Games}},
type = {Masters},
year = {2010}
}

@article{wiering1998fast,
  title={Fast online Q ($\lambda$)},
  author={Wiering, M. and Schmidhuber, J.},
  journal={Machine Learning},
  volume={33},
  number={1},
  pages={105--115},
  year={1998},
  publisher={Springer}
}

@book{mohri2012foundations,
  title={Foundations of Machine Learning},
  author={Mohri, M. and Rostamizadeh, A. and Talwalkar, A.},
  isbn={9780262018258},
  lccn={2012007249},
  series={Adaptive Computation and Machine Learning Series},
  url={http://books.google.com/books?id=w4vuugAACAAJ},
  year={2012},
  publisher={Mit Press}
}

@ARTICLE{bellemare12arcade,
  author = {{Bellemare}, M.~G. and {Naddaf}, Y. and {Veness}, J. and {Bowling}, M.},
  title = {The Arcade Learning Environment: An Evaluation Platform for General Agents},
  journal = {ArXiv e-prints},
  archivePrefix = "arXiv",
  eprint = {1207.4708},
  primaryClass = "cs.AI",
  keywords = {Computer Science - Artificial Intelligence},
  year = 2012,
  month = jul,
}

